title: ParceLiNGAM: a causal ordering method robust against latent confounders.
abstract: We consider learning a causal ordering of variables in a linear nongaussian acyclic model called LiNGAM. Several methods have been shown to consistently estimate a causal ordering assuming that all the model assumptions are correct. But the estimation results could be distorted if some assumptions are violated. In this letter, we propose a new algorithm for learning causal orders that is robust against one typical violation of the model assumptions: latent confounders. The key idea is to detect latent confounders by testing independence between estimated external influences and find subsets (parcels) that include variables unaffected by latent confounders. We demonstrate the effectiveness of our method using artificial data and simulated brain imaging data.
authors.affiliation: Institute of Scientific and Industrial Research, Osaka University, Ibaraki, Osaka 567-0047, Japan tashiro@ar.sanken.osaka-u.ac.jp.
authors.forename: Tatsuya
authors.initials: T
authors.lastname: Tashiro
authors.forename: Shohei
authors.initials: S
authors.lastname: Shimizu
authors.forename: Aapo
authors.initials: A
authors.lastname: Hyvärinen
authors.forename: Takashi
authors.initials: T
authors.lastname: Washio
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D001921:Brain / Q000502:physiology; D001931:Brain Mapping / Q000379:methods*; D006801:Humans; D008279:Magnetic Resonance Imaging; D008962:Models, Theoretical